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29916-01 - Lecture with practical courses: Advanced Statistics: Multilevel analysis (4 CP)

Semester fall semester 2025
Course frequency Every fall sem.
Lecturers Michael Ketzer (michael.ketzer@unibas.ch)
Michael Simon (m.simon@unibas.ch, Assessor)
Diana Trutschel (diana.trutschel@unibas.ch)
Content Statistics is ubiquitous in medical and nursing research. Clinicians and nurse researchers need to understand basic statistical concepts, be able to interpret statistical results and conduct basic statistical analyses themselves. The course "Advanced Statistics: Multilevel analysis" is the last part of a series of three courses to use the statistical programming language R to learn statistics. This course will provide students the basis to understand and apply advanced statistical techniques such as linear and generalized linear mixed model in the context of nursing research. With the successful completion of the course students will be able to:

1. Understand the conceptual implications of variables on different levels (i.e. micro, meso macro level)
2. Analyze intraclass correlations in the context of LMM and GLMM
3. Analyse random intercept models including variables on different levels
4. Write statistical reports about quantitative analyses conducted in the programming language R
5. Apply principles of reproducible research using R Markdown
Learning objectives • A lecture-seminar-exercise format will be used, with 30-60 minute lectures, 60 minute seminars and 60 minute exercises.
• Introduction to more advanced statistical approaches such as multilevel regression and intraclass correlation
• Practical training in R programming and analytical techniques.
• Analysis of a dataset using R and writing a report using R Markdown in a reproducible manner.
Bibliography Facultative literature:
- Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and Other Stories. Cambridge: Cambridge University Press.
- Snijders, T. A. B., and Bosker, Roel J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. London: Sage Publishers.
Comments Sessions will be made available through Panopto.
Weblink ADAM Login

 

Admission requirements Aufnahme in den Studiengang Pflegewissenschaft
Erfolgreicher Abschluss der LV Statistics I & II
PhD-Studierende können nur nach Zustimmung der Kursleitung teilnehmen und nur nach erfolgreicher Teilnahme an Statistics I & II
Course application in Services belegen
Language of instruction English
Use of digital media Online, mandatory

 

Interval Weekday Time Room
wöchentlich Tuesday 15.15-18.00 Kollegienhaus, Hörsaal 119

Dates

Date Time Room
Tuesday 16.09.2025 15.15-18.00 Kollegienhaus, Hörsaal 119
Tuesday 23.09.2025 15.15-18.00 Kollegienhaus, Hörsaal 119
Tuesday 30.09.2025 15.15-18.00 Kollegienhaus, Hörsaal 119
Tuesday 07.10.2025 15.15-18.00 Kollegienhaus, Hörsaal 119
Tuesday 14.10.2025 15.15-18.00 Kollegienhaus, Hörsaal 119
Tuesday 21.10.2025 15.15-18.00 Kollegienhaus, Hörsaal 119
Tuesday 28.10.2025 15.15-18.00 Kollegienhaus, Hörsaal 119
Tuesday 04.11.2025 15.15-18.00 Kollegienhaus, Hörsaal 119
Tuesday 11.11.2025 15.15-18.00 Kollegienhaus, Hörsaal 119
Tuesday 18.11.2025 15.15-18.00 Kollegienhaus, Hörsaal 119
Tuesday 25.11.2025 15.15-18.00 Kollegienhaus, Hörsaal 119
Tuesday 02.12.2025 15.15-18.00 Kollegienhaus, Hörsaal 119
Tuesday 09.12.2025 15.15-18.00 Kollegienhaus, Hörsaal 119
Tuesday 16.12.2025 15.15-18.00 Kollegienhaus, Hörsaal 119
Modules Modul Vertiefung Research (Master's Studies: Nursing)
Assessment format continuous assessment
Assessment details The exams consist of a written analysis draft (30% of the course grade), a written final analysis (70% of the course grade).
Assessment registration/deregistration Registration: course registration: deregistration: institute
Repeat examination no repeat examination
Scale 1-6 0,1
Repeated registration one repetition
Responsible faculty Faculty of Medicine
Offered by Institut für Pflegewissenschaft

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